E. Hesslow

Bio: E. Hesslow is an academic researcher. The author has contributed to research in topics: Adaptive control & Cruise control. The author has an hindex of 1, co-authored 1 publications receiving 65 citations.

Stop and go controller for adaptive cruise control

Abstract: In the field of vehicle control, conventional cruise control systems have been available on the market for many years. During the last years, modern cars include more and more electronic systems. These systems are often governed by a computer or a network of computers programmed with powerful software. One of those new services is adaptive cruise control (ACC) (or autonomous intelligent cruise control, AICC), which extends the conventional cruise control system to include automated car following when the preceding car is driving at a lower speed than the desired set-speed. The focus of ACC has mainly been directed towards high-speed highway application, but to improve the comfort to the driver also low-speed situations must be considered. The paper presents an ACC system that is capable of car following in low-speed situations, e.g. in suburban areas, as well as in high-speed situations. The system is implemented in a test car and the result is evaluated.

Research advances in intelligent collision avoidance and adaptive cruise control

Abstract: This paper looks into recent developments and research trends in collision avoidance/warning systems and automation of vehicle longitudinal/lateral control tasks. It is an attempt to provide a bigger picture of the very diverse, detailed and highly multidisciplinary research in this area. Based on diversely selected research, this paper explains the initiatives for automation in different levels of transportation system with a specific emphasis on the vehicle-level automation. Human factor studies and legal issues are analyzed as well as control algorithms. Drivers' comfort and well being, increased safety, and increased highway capacity are among the most important initiatives counted for automation. However, sometimes these are contradictory requirements. Relying on an analytical survey of the published research, we will try to provide a more clear understanding of the impact of automation/warning systems on each of the above-mentioned factors. The discussion of sensory issues requires a dedicated paper due to its broad range and is not addressed in this paper.

Model Predictive Multi-Objective Vehicular Adaptive Cruise Control

Abstract: This paper presents a novel vehicular adaptive cruise control (ACC) system that can comprehensively address issues of tracking capability, fuel economy and driver desired response. A hierarchical control architecture is utilized in which a lower controller compensates for nonlinear vehicle dynamics and enables tracking of desired acceleration. The upper controller is synthesized under the framework of model predictive control (MPC) theory. A quadratic cost function is developed that considers the contradictions between minimal tracking error, low fuel consumption and accordance with driver dynamic car-following characteristics while driver longitudinal ride comfort, driver permissible tracking range and rear-end safety are formulated as linear constraints. Employing a constraint softening method to avoid computing infeasibility, an optimal control law is numerically calculated using a quadratic programming algorithm. Detailed simulations with a heavy duty truck show that the developed ACC system provides significant benefits in terms of fuel economy and tracking capability while at the same time also satisfying driver desired car following characteristics.

A Safe Longitudinal Control for Adaptive Cruise Control and Stop-and-Go Scenarios

Abstract: In this paper, we propose a novel reference model-based control approach for automotive longitudinal control. The reference model is nonlinear and provides dynamic solutions consistent with safety constraints and comfort specifications. The model is based on physical laws of compliant contact and has the particularity that its solutions can be explicitly described by integral curves. This allows to characterize the set of initial condition for which the constraints can be met. This model is combined with a simple feedback loop used to compensate unmodeled dynamics and external disturbances. Model simulations together with experimental results are also presented

Adaptive fuzzy control for inter-vehicle gap keeping

Abstract: There is a broad range of diverse technologies under the generic topic of intelligent transportation systems (ITS) that holds the answer to many of the transportation problems. In this paper, one approach to ITS is presented. One of the most important research topics in this field is adaptive cruise control (ACC). The main features of this kind of controller are the adaptation of the speed of the car to a predefined one and the keeping of a safe gap between the controlled car and the preceding vehicle on the road. We present an ACC controller based on fuzzy logic, which assists the speed and distance vehicle control, offering driving strategies and actuation over the throttle of a car. The driving information is supplied by the car tachometer and a RTK differential GPS, and the actuation over the car is made through an electronic interface that simulates the electrical signal of the accelerator pedal directly to the onboard computer. This control is embedded in an automatic driving system installed in two testbed mass-produced cars instrumented for testing the work of these controllers in a real environment. The results obtained in these experiments show a very good performance of the gap controller, which is adaptable to all the speeds and safe gap selections.

Traffic control and intelligent vehicle highway systems: a survey

Abstract: Traffic congestion in highway networks is one of the main issues to be addressed by today's traffic management schemes. Automation combined with the increasing market penetration of on-line communication, navigation and advanced driver assistance systems will ultimately result in intelligent vehicle highway systems (IVHS) that distribute intelligence between roadside infrastructure and vehicles and that - in particular on the longer term - are one of the most promising solutions to the traffic congestion problem. In this study, the authors present a survey on traffic management and control frameworks for IVHS. First, they give a short overview of the main currently used traffic control methods for freeways. Next, they discuss IVHS-based traffic control measures. Then, various traffic management architectures for IVHS such as PATH, Dolphin, Auto21 CDS etc. are discussed and a comparison of the various frameworks is presented. Finally, the authors sketch how existing traffic control methodologies could fit in an IVHS-based traffic control set-up. © 2011 The Institution of Engineering and Technology.